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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Published on: October 13, 2023

Sampling properties of directed networks.

S-W Son1, C Christensen, G Bizhani

  • 1Complexity Science Group, University of Calgary, Calgary T2N 1N4, Canada. sonswoo@hanyang.ac.kr

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 11, 2012
PubMed
Summary
This summary is machine-generated.

Breadth-first search (BFS) sampling significantly distorts network topology, overestimating key metrics like degree and reciprocity. Accurate analysis of directed networks requires higher sampling coverage (over 65%) or alternative methods.

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Area of Science:

  • Network Science
  • Data Science
  • Computational Social Science

Background:

  • Real-world network analysis often relies on sampled data, not complete systems.
  • Breadth-first search (BFS) sampling is a common but potentially biased method for network analysis.

Purpose of the Study:

  • To systematically evaluate the impact of sampling methods and coverage on directed network topology.
  • To compare BFS sampling with random sampling on large-scale directed networks.

Main Methods:

  • Comparison of BFS and random sampling techniques.
  • Analysis of topological properties on seven diverse, complete directed networks before and after sampling.
  • Evaluation of sampling effects on bow-tie structure, strongly connected components, degree distribution, and reciprocity.

Main Results:

  • BFS sampling significantly alters network topological properties, including the bow-tie structure and strongly connected components.
  • Low sampling coverage (<40%) leads to overestimations of average degree, out-degree variance, degree autocorrelation, and link reciprocity by over 30%.
  • Accurate estimations require sampling coverage exceeding 65% for BFS-sampled networks.

Conclusions:

  • The choice of sampling method and coverage critically impacts the perceived structure and properties of directed networks.
  • Current understanding of real-world directed network structure, function, and evolution may need revision due to sampling biases.
  • Rethinking network analysis methodologies is crucial for accurate insights into complex systems.